Kuchenbecker Lindsey A, Thompson Kevin J, Hurst Cheyenne D, Opdenbosch Bianca M, Heckman Michael G, Reddy Joseph S, Nguyen Thuy, Casellas Heidi L, Sotelo Katie D, Reddy Delila J, Lucas John A, Day Gregory S, Willis Floyd B, Graff-Radford Neill, Ertekin-Taner Nilufer, Kalari Krishna R, Carrasquillo Minerva M
Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
Center for Clinical and Translational Science, Mayo Clinic, Rochester, MN, USA.
bioRxiv. 2024 Jul 29:2024.07.27.605373. doi: 10.1101/2024.07.27.605373.
African Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimer's disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD.
Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy.
A plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94).
This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.
非裔美国人（AA）在阿尔茨海默病（AD）的血浆生物标志物研究中代表性严重不足，且目前的诊断生物标志物候选物并未反映出AD的异质性。
使用SomaScan 7k平台进行非靶向蛋白质组测量，以在一组临床诊断为AD痴呆（n = 183）或认知未受损（CU，n = 145）的非裔美国人队列中鉴定AD的新型血浆生物标志物。采用机器学习方法来确定产生最佳分类准确性的血浆蛋白集。
一个血浆蛋白组在区分AD痴呆与CU时，曲线下面积（AUC）达到0.91。这一发现的可重复性在ANMerge血浆和AMP-AD多样性脑数据集中得到观察（AUC = 0.83；AUC = 0.94）。
本研究证明了通过非靶向血浆蛋白质组学和机器学习方法发现生物标志物的潜力。我们的研究结果还突出了基质组和脑血管功能障碍在AD病理生理学中的潜在重要性。
